This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

In this paper, we develop position embedding transformation (PETR) for multi-view 3D
object detection. PETR encodes the position information of 3D coordinates into image …

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

Detection identifies objects as axis-aligned boxes in an image. Most successful object
detectors enumerate a nearly exhaustive list of potential object locations and classify each …

We introduce a framework for multi-camera 3D object detection. In contrast to existing works,
which estimate 3D bounding boxes directly from monocular images or use depth prediction …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The
whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal …

The goal of perception for autonomous vehicles is to extract semantic representations from
multiple sensors and fuse these representations into a single “bird's-eye-view” coordinate …